Course syllabus ASM - Applied Statistical Methods (FBE - SS 2019/2020)


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Course code: ASM
Course title in language of instruction: Aplikované statistické metody
Course title in Czech: Applied Statistical Methods
Course title in English: Applied Statistical Methods
Mode of completion and number of credits: Exam (5 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes: full-time, 2/2 (hours of lectures per week / hours of seminars per week)
Language of instruction: Czech
Level of course: master continuing
Semester: SS 2019/2020
Name of lecturer: doc. Ing. Václav Adamec, Ph.D. (examiner, instructor, lecturer, supervisor)
Prerequisites: Uznaný předmět pro obor REO
 
Aims of the course:
Obtaining theoretical knowledge and practical experience with advanced statistical methods by students and their application in business management and individual functions of trade. Knowledge and skills learned by students in the course will be used to carry out diploma research (project) and prepare diploma thesis.
 
Course contents:
1.Introduction to statistical methods (Statistica 12) (allowance 2/2)
 
a.Statistics, methods, basic terminology.
b.Simple and interval grouping, frequency tables a diagrams.
c.Testing for presence of outliers, handling missing values.

2.Testing statistical hypothesis (Statistica 12) (allowance 4/4)
 
a.Principles of statistical hypothesis test.
b.Type I. and Type II. error in statistical tests, power of statistical test, significance, p-value.
c.Tests for normality: SW, KS, GOF, AD and JB tests.
d.Hypothesis tests for the parameters of the normal, Bernoulli and exponential distributions; one-sample and two-sample tests.

3.Nonparametric tests (Statistica 12) (allowance 4/4)
 
a.Parametric and non-parametric tests: principles, assumptions, construction, application.
b.One-sample location tests: sign test, Wilcoxon signed rank test, application on two dependent samples.
c.Two-sample location tests (independent samples): Mann-Whitney test.
d.Nonparametric Anova and related tests: Kruskal-Wallis, median test, Friedman test.
e.Post-hoc tests: Dunn, Tukey, Neményi, Conover.
f.Nonparametric correlation: methods of Spearman and Kendal.

4.Analysis of variance with applications (Statistica 12) (allowance 4/4)
 
a.One-way analysis of variance: assumptions, linear model, global F-test.
b.Post-hoc tests: Tukey HSD, Fisher LSD, Scheffé procedure, p-value adjustment (Bonferroni method).
c.Tests of linear restrictions: method of contrast, F-test and t-test variants.
d.Two-way analysis of variance, with (or without) interaction.
e.Simple effects, main effects, interactions, tests of significance.

5.Analysis of categorical data (Statistica 12) (allowance 4/4)
 
a.Categorical data, tabulation, contingency table, description.
b.Independence tests: Pearson chi-square test, asymptotic LR test.
c.Contingency coefficients: Pearson, Cramér, Tschuprov, Phi.
d.2 x 2 association tables, independence tests.
e.Special case handling: McNemar test, Fisher exact test.
f.Binary logit (or probit) model: principle of construction, estimation, verification, interpretation.

6.Linear model applications to economic problems (Statistica 12) (allowance 2/2)
 
a.Method of Difference-in-Difference (DID).
b.Principles, assumptions, linear model, DID estimator, application.

7.Intro to analysis of univariate economic time series (Gretl) (allowance 2/2)
 
a.Time series stationarity: weak and strong definition.
b.Non-stationarity in time series: deterministic and stochastic trend.
c.Unit root tests: DF, ADF, KPSS, PP.

8.Box and Jenkins models of stochastic time series (Gretl) (allowance 3/4)
 
a.Non-seasonal autoregression AR(p) process, process of moving averages MA(q).
b.Combined process of ARMA(p,q).
c.Models of integrated time series ARIMA(p,d,q).
d.Seasonal processes SAR(P), SMA(Q) and SARMA(P,Q).
e.Specification, estimation, verification and application of SARIMA model.

9.Analysis of multivariate time series (Gretl) (allowance 3/3)
 
a.Non-stationarity in time series and problem of spurious correlation.
b.Cointegration: principles and verification. Engle-Granger test, Johansen test.
c.Models of nonstationary time series (ECM).
d.VAR(p) model: principles, specification, estimation, verification, application.
e.Granger causality test.
f.Impulse - response analysis, forecast error variance decomposition.

 
Learning outcomes and competences:
Generic competences:
 
-Ability to analyse and synthesize
-Ability to create new ideas (creativity)
-Ability to solve problems
-Science and research skills
-Skilled at utilizing and processing information

Specific competences:
 
-Student is able solve the assigned task and write the scientific report about the solution
-Student is able to apply Statistica 12 software during statistical analysis of economic data.
-Student understands and is able to correctly apply advanced statistical methods, both parametric and nonparametric.
-Student understands principles of testing statistical hypotheses; discern Type I. Error from Type II. Error.

Type of course unit: required
Year of study: Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement: There is no compulsory work placement in the course unit.
Recommended study modules: -
 
Learning activities and study load (hours of study load):
Type of teaching methodDaily attendance
Direct teaching
     lecture28 h
     practice28 h
     consultation0 h
Self-study
     preparation for exam36 h
     elaboration and execution of projects48 h
Total140 h
 
Assessment methods:
A credit is granted on the basis of two projects (>= 50% score) and active participation in PC labs (max. 2 skipped labs allowed). The credit is required for admission to the final exam. Passing final exam requires at least 50% point score. Course grade is made on the basis of the final exam and project scores: A [73 – 82]; B [65 – 73); C [57 – 65); D [49 – 57); E [41 – 49); F [0 – 41). The examiner may adjust the grade by 1 step in both directions. The course cannot be taken during overseas internship.
 
Recommended reading:
TypeAuthorTitlePublished inPublisherYearISBN
RQBLAŠKOVÁ, V. -- STEHLÍKOVÁ, B. -- MARKECHOVÁ, D. -- TIRPÁKOVÁ, A. -- STŘELEC, L.Statistika IIBrnoMZLU v Brně2009978-80-7375-296-5
RQBUDÍKOVÁ, M. -- KRÁLOVÁ, M. -- MAROŠ, B.Průvodce základními statistickými metodamiPrahaGrada2010978-80-247-3243-5
RQHENDL , J.Přehled statistických metodPrahaPortál s.r.o. 2015978-80-262-0981-2
RQNEUBAUER, J. -- SEDLAČÍK, M. -- KŘÍŽ, O.Základy statistiky: aplikace v technických a ekonomických oborechPrahaGrada2016978-80-247-5786-5
READAMEC, V. -- STŘELEC, L. -- HAMPEL, D.Ekonometrie I: učební text978-80-7375-703-8
REARLT, J. -- ARLTOVÁ, M.Ekonomické časové řady: [vlastnosti, metody modelování, příklady a aplikace]PrahaGrada2007978-80-247-1319-9
REHEBÁK, P. et al.Statistické myšlení a nástroje analýzy datPrahaInformatorum2013978-80-7333-105-4
REHENDL, J.Kvalitativní výzkum: základní teorie, metody a aplikacePrahaPortál2012978-80-262-0219-6
REMAREK, L. et al.Statistika pro ekonomy: aplikacePrahaProfessional Publishing2007978-80-86946-40-5
REMELOUN, M. -- MILITKÝ, J. -- HILL, M.Statistická analýza vícerozměrných dat v příkladechPrahaAcademia2012978-80-200-2071-0

RQrequired
RErecommended


Last modification made by Ing. Jiří Gruber on 12/04/2019.

Type of output: